Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

A misspecification test for multiplicative error models of non-negative time series processes

Gao, Jiti, Kim, Nam Hyun and Saart, Patrick W. ORCID: 2015. A misspecification test for multiplicative error models of non-negative time series processes. Journal of Econometrics 189 (2) , pp. 346-359. 10.1016/j.jeconom.2015.03.028

Full text not available from this repository.


In recent years, analysis of financial time series focuses largely on data related to market trading activity. Apart from modeling of the conditional variance of returns within the generalized autoregressive conditional heteroskedasticity (GARCH) family of models, presently attention is also devoted to that of other market variables, for instance volumes, number of trades or financial durations. To this end, a large group of researchers focus their studies on a class of model that is referred to in the literature as the multiplicative error model (MEM), which is considered particularly for modeling non-negative time series processes. The goal of the current paper is to establish an alternative misspecification test for the MEM of non-negative time series processes. In the literature, although several procedures are available to perform hypothesis testing for the MEM, the newly proposed testing procedure is particularly useful in the context of the MEM of waiting times between financial events since its outcomes have a number of important implications on the fundamental concept of point processes. Finally, the current paper makes a number of statistical contributions, especially in making a head way into nonparametric hypothesis testing of unobservable variables.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Publisher: Elsevier
ISSN: 0304-4076
Date of First Compliant Deposit: 14 November 2018
Date of Acceptance: 1 July 2015
Last Modified: 24 Oct 2022 08:05

Citation Data

Cited 10 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item